Dual-energy CT Radiomics Combined with Quantitative Parameters for Differentiating Lung Adenocarcinoma From Squamous Cell Carcinoma: A Dual-center Study.

RATIONALE AND OBJECTIVES: To evaluate the ability of dual-energy CT(DECT)-based quantitative parameters and radiomics features to differentiate solid lung adenocarcinoma (ADC) from squamous cell carcinoma (SCC). METHODS: This study included 213 patients diagnosed with ADC and SCC who underwent DECT scans at two centers from November 2022 to December 2023. Patients at center 1 were randomly divided into training (n = 114) and internal test set (n = 50) in a 7:3 ratio, with center 2 serving as the external test set (n = 49). Radiologic and clinical data were combined to establish a clinical-radiologic model. Ten types of DECT energy images including conventional images, iodine density (ID), effective atomic number (Zeff), electron density, and virtual mono-energetic images (VMI) were reconstructed in both arterial phases (AP) and venous phases (VP). Quantitative parameters were measured at the uniform enhanced solid portion of the tumor and normalized to the aorta, used to develop a quantification model and calculate the quantitative score (quantscore). Radiologists manually delineated the tumor ROI at the largest level for extracting radiomics features in these 10 energy images. These features were used to establish 10 uni-energy models from which the best-performing features were selected to construct the final radiomics model and calculate a radiomics score (radscore). Then, a combined model was developed using the akaike information criterion(AIC) and compared to the clinical-radiological model to test its diagnostic validity. RESULTS: The independent predictors of the clinical-radiological model included age, gender, and central or peripheral location, and the AUCs for the training set, internal test set, and external test set were 0.808, 0.837, and 0.802. The quantification model incorporated 40 keV CT values, Zeff, normalized Zeff, and the slope of the spectral attenuation curve (λHU) in the AP and normalized ID, Zeff, and λHU in the VP. Uni-energy models based on AP ID maps, AP Zeff maps, and VP VMI 65 keV significantly outperformed AUC= 0.5, and 11 radiomics features were selected from these three models to construct the final radiomics model. The combined model, incorporating age, gender, quantscore, and radscore, significantly outperformed the clinical-radiological model in the training set (AUC=0.952 vs 0.808, P < 0.001), and demonstrated higher performance in both the internal and external test sets, although these differences did not reach statistical significance (AUC=0.870 vs 0.837, for the internal test set [P = 0.542], 0.888 vs 0.802 for the external test sets [P = 0.128]). The evaluation of the combined model demonstrated good discriminative ability and potential for generalization. CONCLUSION: The combined model, integrating quantitative parameters and radiomics features from DECT multi-energy images with clinical-radiological characteristics, can be used as a non-invasive tool to differentiate ADC from SCC.

Application of machine learning for the differentiation of thymomas and thymic cysts using deep transfer learning: A multi-center comparison of diagnostic performance based on different dimensional models.

OBJECTIVE: This study aimed to evaluate the feasibility and performance of deep transfer learning (DTL) networks with different types and dimensions in differentiating thymomas from thymic cysts in a retrospective cohort. MATERIALS AND METHODS: Based on chest-enhanced computed tomography (CT), the region of interest was delineated, and the maximum cross section of the lesion was selected as the input image. Five convolutional neural networks (CNNs) and Vision Transformer (ViT) were used to construct a 2D DTL model. The 2D model constructed by the maximum section (n) and the upper and lower layers (n - 1, n + 1) of the lesion was used for feature extraction, and the features were selected. The remaining features were pre-fused to construct a 2.5D model. The whole lesion image was selected for input and constructing a 3D model. RESULTS: In the 2D model, the area under curve (AUC) of Resnet50 was 0.950 in the training cohort and 0.907 in the internal validation cohort. In the 2.5D model, the AUCs of Vgg11 in the internal validation cohort and external validation cohort 1 were 0.937 and 0.965, respectively. The AUCs of Inception_v3 in the training cohort and external validation cohort 2 were 0.981 and 0.950, respectively. The AUC values of 3D_Resnet50 in the four cohorts were 0.987, 0.937, 0.938, and 0.905. CONCLUSIONS: The DTL model based on multiple different dimensions can be used as a highly sensitive and specific tool for the non-invasive differential diagnosis of thymomas and thymic cysts to assist clinicians in decision-making.

CT-based radiomics research for discriminating the risk stratification of pheochromocytoma using different machine learning models: a multi-center study.

OBJECTIVES: The purpose of this study was to explore and verify the value of various machine learning models in preoperative risk stratification of pheochromocytoma. METHODS: A total of 155 patients diagnosed with pheochromocytoma through surgical pathology were included in this research (training cohort: n = 105; test cohort: n = 50); the risk stratification scoring system classified a PASS score of < 4 as low risk and a PASS score of ≥ 4 as high risk. From CT images captured during the non-enhanced, arterial, and portal venous phase, radiomic features were extracted. After reducing dimensions and selecting features, Logistic Regression (LR), Extra Trees, and K-Nearest Neighbor (KNN) were utilized to construct the radiomics models. By adopting ROC curve analysis, the optimal radiomics model was selected. Univariate and multivariate logistic regression analyses of clinical radiological features were used to determine the variables and establish a clinical model. The integration of radiomics and clinical features resulted in the creation of a combined model. ROC curve analysis was used to evaluate the performance of the model, while decision curve analysis (DCA) was employed to assess its clinical value. RESULTS: 3591 radiomics features were extracted from the region of interest in unenhanced and dual-phase (arterial and portal venous phase) CT images. 13 radiomics features were deemed to be valuable. The LR model demonstrated the highest prediction efficiency and robustness among the tested radiomics models, with an AUC of 0.877 in the training cohort and 0.857 in the test cohort. Ultimately, the composite of clinical features was utilized to formulate the clinical model. The combined model demonstrated the best discriminative ability (AUC, training cohort: 0.887; test cohort: 0.874). The DCA of the combined model showed the best clinical efficacy. CONCLUSION: The combined model integrating radiomics and clinical features had an outstanding performance in differentiating the risk of pheochromocytoma and could offer a non-intrusive and effective approach for making clinical decisions.

Development and Validation of Contrast-Enhanced CT-Based Deep Transfer Learning and Combined Clinical-Radiomics Model to Discriminate Thymomas and Thymic Cysts: A Multicenter Study.

RATIONALE AND OBJECTIVES: This study aims to evaluate the feasibility and effectiveness of deep transfer learning (DTL) and clinical-radiomics in differentiating thymoma from thymic cysts. MATERIALS AND METHODS: Clinical and imaging data of 196 patients pathologically diagnosed with thymoma and thymic cysts were retrospectively collected from center 1. (training cohort: n = 137; internal validation cohort: n = 59). An independent external validation cohort comprised 68 thymoma and thymic cyst patients from center 2. Region of interest (ROI) delineation was performed on contrast-enhanced chest computed tomography (CT) images, and eight DTL models including Densenet 169, Mobilenet V2, Resnet 101, Resnet 18, Resnet 34, Resnet 50, Vgg 13, Vgg 16 were constructed. Radiomics features were extracted from the ROI on the CT images of thymoma and thymic cyst patients, and feature selection was performed using intra-observer correlation coefficient (ICC), Spearman correlation analysis, and least absolute shrinkage and selection operator (LASSO) algorithm. Univariate analysis and multivariable logistic regression (LR) were used to select clinical-radiological features. Six machine learning classifiers, including LR, support vector machine (SVM), k-nearest neighbors (KNN), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost), and Multilayer Perceptron (MLP), were used to construct Radiomics and Clinico-radiologic models. The selected features from the Radiomics and Clinico-radiologic models were fused to build a Combined model. Receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA) were used to evaluate the discrimination, calibration, and clinical utility of the models, respectively. The Delong test was used to compare the AUC between different models. K-means clustering was used to subdivide the lesions of thymomas or thymic cysts into subregions, and traditional radiomics methods were used to extract features and compare the ability of Radiomics and DTL models to reflect intratumoral heterogeneity using correlation analysis. RESULTS: The Densenet 169 based on DTL performed the best, with AUC of 0.933 (95% CI: 0.875-0.991) in the internal validation cohort and 0.962 (95% CI: 0.923-1.000) in the external validation cohort. The AdaBoost classifier achieved AUC of 0.965 (95% CI: 0.923-1.000) and 0.959 (95% CI: 0.919-1.000) in the internal and external validation cohorts, respectively, for the Radiomics model. The LightGBM classifier achieved AUC of 0.805 (95% CI: 0.690-0.920) and 0.839 (95% CI: 0.736-0.943) in the Clinico-radiologic model. The AUC of the Combined model in the internal and external validation cohorts was 0.933 (95% CI: 0.866-1.000) and 0.945 (95% CI: 0.897-0.994), respectively. The results of the Delong test showed that the Radiomics model, DTL model, and Combined model outperformed the Clinico-radiologic model in both internal and external validation cohorts (p-values were 0.002, 0.004, and 0.033 in the internal validation cohort, while in the external validation cohort, the p-values were 0.014, 0.006, and 0.015, respectively). But there was no statistical difference in performance among the three models (all p-values <0.05). Correlation analysis showed that radiomics performed better than DTL in quantifying intratumoral heterogeneity differences between thymoma and thymic cysts. CONCLUSION: The developed DTL model and the Combined model based on radiomics and clinical-radiologic features achieved excellent diagnostic performance in differentiating thymic cysts from thymoma. They can serve as potential tools to assist clinical decision-making, particularly when endoscopic biopsy carries a high risk.

Prediction of Central Lymph Node Metastasis in cN0 Papillary Thyroid Carcinoma by CT Radiomics.

RATIONALE AND OBJECTIVES: To explore the feasibility of the preoperative prediction of pathological central lymph node metastasis (CLNM) status in patients with negative clinical lymph node (cN0) papillary thyroid carcinoma (PTC) using a computed tomography (CT) radiomics signature. MATERIALS AND METHODS: A total of 97 PTC cN0 nodules with CLNM pathology data (pN0, with CLNM, n = 59; pN1, without CLNM, n = 38) in 85 patients were divided into a training set (n = 69) and a validation set (n = 28). For each lesion, 321 radiomic features were extracted from nonenhanced, arterial and venous phase CT images. Minimum redundancy and maximum relevance and the least absolute shrinkage and selection operator were used to find the most important features with which to develop a radiomics signature in the training set. The performance of the radiomics signature was evaluated by receiver operating characteristic curves, calibration curves and decision curve analysis . RESULTS: Three nonzero the least absolute shrinkage and selection operator coefficient features were selected for radiomics signature construction. The radiomics signature for distinguishing the pN0 and pN1 groups achieved areas under the curve of 0.79 (95% CI 0.67, 0.91) in the training set and 0.77 (95% CI 0.55, 0.99) in the validation set. The calibration curves demonstrated good agreement between the radiomics score-predicted probability and the pathological results in the two sets (p= 0.399, p = 0.191). The decision curve analysis curves showed that the model was clinically useful. CONCLUSION: This radiomic signature could be helpful to predict CLNM status in cN0 PTC patients.

Development and Validation of a Comprehensive Model for Predicting Distant Metastasis of Solid Lung Adenocarcinoma: 3D Radiomics, 2D Radiomics and Clinical Features.

Objective: To develop and validate models for predicting distant metastases in patients with solid lung adenocarcinomas using 3D radiomic features, 2D radiomic features, clinical features, and their combinations. Methods: This retrospective study included 253 eligible patients with solid adenocarcinoma of the lung diagnosed at our hospital between August 2018 and August 2021. 3D and 2D regions of interest were segmented from computed tomography-enhanced thin-slice images of the venous phase, and 851 radiomic features were extracted in each region. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was used to select radiomic features and calculate radiomic scores, and logistic regression was used to develop the model. Development of a 3D radiomics model (model 1), a 2D radiomics model (model 2), a combined 3D radiomics and 2D radiomics model (model 3), a clinical model (model 4), and a comprehensive model (model 5) for the prediction of distant metastases in patients with solid lung adenocarcinomas. Nomograms were drawn to illustrate model 5, and receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used for model evaluation. Results: The AUC (area under the curve) of model 1, model 2, model 3, model 4, and model 5 in the test set was 0.711, 0.769, 0.775, 0.829, and 0.892, respectively. The Delong test showed that AUC values were statistically different between model 5 and model 1 (p=0.001), and there was no statistical difference in AUC between the other models. Based on a comprehensive review of DCA, ROC curve, and Akaike information criterion (AIC), Model 5 is demonstrated to have better clinical utility, goodness of fit, and parsimony. Conclusion: A comprehensive model based on 3D radiomic features, 2D radiomic features, and clinical features has the potential to predict distant metastasis in patients with solid lung adenocarcinomas.

Development and validation of a radiomic nomogram based on pretherapy dual-energy CT for distinguishing adenocarcinoma from squamous cell carcinoma of the lung.

Objective: Based on pretherapy dual-energy computed tomography (DECT) images, we developed and validated a nomogram combined with clinical parameters and radiomic features to predict the pathologic subtypes of non-small cell lung cancer (NSCLC) - adenocarcinoma (ADC) and squamous cell carcinoma (SCC). Methods: A total of 129 pathologically confirmed NSCLC patients treated at the Second Affiliated Hospital of Nanchang University from October 2017 to October 2021 were retrospectively analyzed. Patients were randomly divided in a ratio of 7:3 (n=90) into training and validation cohorts (n=39). Patients' pretherapy clinical parameters were recorded. Radiomics features of the primary lesion were extracted from two sets of monoenergetic images (40 keV and 100 keV) in arterial phases (AP) and venous phases (VP). Features were selected successively through the intra-class correlation coefficient (ICC) and the least absolute shrinkage and selection operator (LASSO). Multivariate logistic regression analysis was then performed to establish predictive models. The prediction performance between models was evaluated and compared using the receiver operating characteristic (ROC) curve, DeLong test, and Akaike information criterion (AIC). A nomogram was developed based on the model with the best predictive performance to evaluate its calibration and clinical utility. Results: A total of 87 ADC and 42 SCC patients were enrolled in this study. Among the five constructed models, the integrative model (AUC: Model 4 = 0.92, Model 5 = 0.93) combining clinical parameters and radiomic features had a higher AUC than the individual clinical models or radiomic models (AUC: Model 1 = 0.84, Model 2 = 0.79, Model 3 = 0.84). The combined clinical-venous phase radiomics model had the best predictive performance, goodness of fit, and parsimony; the area under the ROC curve (AUC) of the training and validation cohorts was 0.93 and 0.90, respectively, and the AIC value was 60.16. Then, this model was visualized as a nomogram. The calibration curves demonstrated it's good calibration, and decision curve analysis (DCA) proved its clinical utility. Conclusion: The combined clinical-radiomics model based on pretherapy DECT showed good performance in distinguishing ADC and SCC of the lung. The nomogram constructed based on the best-performing combined clinical-venous phase radiomics model provides a relatively accurate, convenient and noninvasive method for predicting the pathological subtypes of ADC and SCC in NSCLC.

Primary pulmonary T-cell lymphoma after operation for small intestinal stromal tumor: A case report.

Background: The risk of gastrointestinal stromal tumor (GIST) in combination with other primary malignancies is high, which occurs before and after the diagnosis of GIST. Primary pulmonary T-cell lymphoma is a rare type of non-Hodgkin lymphoma. Case presentation: We report a 53-year-old male patient who was admitted to our hospital with fever, cough, and expectoration for 2 weeks. Chest computed tomography (CT) showed a cavitary mass in the left lower lobe with multiple nodules in the upper lobes of both lungs. The patient had a history of surgery for small intestinal stromal tumors and was treated with oral imatinib after surgery. Lung biopsy was diagnosed as lymphomatoid granulomatosis, tending to grade 3. The pathological diagnosis was corrected by surgery and genetic testing for lung non-Hodgkin CD8-positive cytotoxic T-cell lymphoma with Epstein-Barr virus (EBV) infection in some cells. After multiple chemotherapies, the CT scan showed a better improvement than before. The patient is still under follow-up, and no tumor recurrence has been found. Conclusion: Patients with a history of GIST should be monitored for other malignancies. The clinical symptoms and imaging examinations of primary pulmonary T-cell lymphoma are not characteristic, and the definite diagnosis still depends on pathological examination. The patient was treated with the CHOP chemotherapy regimen after the operation, the curative effect was good.

Identification of pulmonary adenocarcinoma and benign lesions in isolated solid lung nodules based on a nomogram of intranodal and perinodal CT radiomic features.

To develop and validate a predictive model based on clinical radiology and radiomics to enhance the ability to distinguish between benign and malignant solitary solid pulmonary nodules. In this study, we retrospectively collected computed tomography (CT) images and clinical data of 286 patients with isolated solid pulmonary nodules diagnosed by surgical pathology, including 155 peripheral adenocarcinomas and 131 benign nodules. They were randomly divided into a training set and verification set at a 7:3 ratio, and 851 radiomic features were extracted from thin-layer enhanced venous phase CT images by outlining intranodal and perinodal regions of interest. We conducted preprocessing measures of image resampling and eigenvalue normalization. The minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (lasso) methods were used to downscale and select features. At the same time, univariate and multifactorial analyses were performed to screen clinical radiology features. Finally, we constructed a nomogram based on clinical radiology, intranodular, and perinodular radiomics features. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC), and the clinical decision curve (DCA) was used to evaluate the clinical practicability of the models. Univariate and multivariate analyses showed that the two clinical factors of sex and age were statistically significant. Lasso screened four intranodal and four perinodal radiomic features. The nomogram based on clinical radiology, intranodular, and perinodular radiomics features showed the best predictive performance (AUC=0.95, accuracy=0.89, sensitivity=0.83, specificity=0.96), which was superior to other independent models. A nomogram based on clinical radiology, intranodular, and perinodular radiomics features is helpful to improve the ability to predict benign and malignant solitary pulmonary nodules.

A Single Enhanced Dual-Energy CT Scan May Distinguish Lung Squamous Cell Carcinoma From Adenocarcinoma During the Venous phase.

RATIONALE AND OBJECTIVES: To investigate whether iodine quantification extracted from enhanced dual energy-computed tomography (DE-CT) is useful for distinguishing lung squamous cell carcinoma from adenocarcinoma and to evaluate whether a single scan evaluated during the venous phase (VP) can be substituted for scans evaluated during other phases. MATERIALS AND METHODS: Sixty-two patients with lung cancer (32 squamous cell carcinomas; 30 adenocarcinomas) underwent enhanced dual-phase DE-CT scans, including an arterial phase and VP. The iodine concentration (IC), normalized iodine concentration (NIC), and slope of the curve (K) in lesions were measured during two scanning phases in two different pathological types of lung cancers. The differences in parameters (IC, NIC, and K) between these two types of lung cancers were statistically analyzed. In addition, the receiver operating characteristic curves of these parameters were performed to discriminate squamous cell carcinoma from adenocarcinoma. RESULTS: The mean IC, NIC, and K in adenocarcinomas were all higher than those in squamous cell carcinomas during the two scanning phases. However, the differences in these parameters between the two types of cancers were significant only during the VP, not during the arterial phase. Receiver operating characteristic analysis demonstrated that the optimal thresholds of the IC, NIC, and K for discriminating squamous cell carcinoma from adenocarcinoma were 1.550, 0.227, and 1.608, respectively. In addition, the sensitivity, specificity, and area under the curve were 81.2%, 83.3%, and 0.871 for the IC; 56.2%, 93.3%, and 0.800 for the NIC; and 65.6%, 80%, and 0.720 for the K; 81.3%, 83.3%, and 0.874 for the IC + NIC; 68.8%, 93.3%, and 0.891 for the IC + NIC + K, respectively. The "IC + NIC + K" had the highest diagnostic efficiency for discriminating two types of lung cancers, but with low sensitivity. Whereas, "IC"and "IC + NIC" had the similar lower diagnostic efficiency, but with high sensitivity and specificity. CONCLUSION: The iodine quantification parameters derived from enhanced DE-CT during the VP may be useful for distinguishing lung squamous cell carcinoma from adenocarcinoma.

Frequency-dependent neural activity in primary angle-closure glaucoma.

OBJECTIVE: In this study, we aimed to investigate the frequency-dependent spontaneous neural activity in primary angle-closure glaucoma (PACG) using the amplitude of low-frequency fluctuations (ALFF) method. PATIENTS AND METHODS: In total, 52 PACG individuals (24 males and 28 females) and 52 normal-sighted controls (NS; 24 males and 28 females) who were closely matched in age, sex, and education underwent resting-state magnetic resonance imaging scans. A repeated-measures ANOVA and post hoc two-sample t-tests were conducted to analyze the different ALFF values in two different frequency bands (slow-4, 0.027-0.073 Hz and slow-5, 0.010-0.027 Hz) between the two groups. Pearson's correlation analysis was conducted to reveal the relationship between the mean ALFF values and clinical variables in the PACG group. RESULTS: Compared to the NS group, the PACG group had high ALFF values in the right inferior occipital gyrus and low ALFF values in the left middle occipital gyrus, left precentral gyrus, and left postcentral gyrus in the slow-4 band. The PACG group had high ALFF values in the right inferior occipital gyrus and low ALFF values in the left inferior parietal lobule, left postcentral gyrus, and right precentral/postcentral gyrus in the slow-5 band. Specifically, we found that the abnormal ALFF values in the bilateral posterior cingulate gyrus and bilateral precuneus were higher in the slow-4 than in the slow-5 band, whereas ALFF in the bilateral frontal lobe, right fusiform, and right cerebellum posterior lobe were higher in the slow-5 than in the slow-4 band. The greater mean ALFF values of the right inferior occipital gyrus were associated with smaller retinal nerve fiber layer thickness and greater visual fields in PACG group in the slow-4 band. CONCLUSION: Our results highlighted that individuals in the PACG group showed abnormal spontaneous neural activities in the visual cortices, sensorimotor cortices, frontal lobe, frontoparietal network, and default mode network at two frequency bands, which might indicate impaired vision and cognition and emotion function in PACG individuals. These findings offer important insight into the understanding of the neural mechanism of PACG.

The mode of yearly biological cycle of malignant growth of hepatocellular carcinoma on computed tomography.

OBJECTIVE: To detect the mode of yearly biological cycle of malignant biological behavior of hepatocellular carcinoma (HCC). METHODS: Twenty-one kinds of CT signs reflecting various degrees of malignant biological behavior were determined. A total of 360 patients were collected by random sampling of 30 patients each month. CT signs of each patient were fitted in corresponding group of yearly cyclic data respectively by cosine curves and analysed in terms of sequence characteristics (cosinor). RESULTS: With a 95% confidence, 10 CT signs showed biological rhythm (P<0.05). The acrophase of CT features for highly-invasive growth concentrated between -60 degree and -120 degree from March to April. For low-invasive growth, however, the CT features were relatively low and concentrated between -180 degrees and -270 degrees from July to September. No acrophase was shown between -120 degrees and -180 degrees from May to June, and between -270 degrees and -330 degrees from October to November. CONCLUSION: Between CT signs of the highly-invasive and those of low-invasive growth of HCC, a sequential difference in biological cycles can be observed.

[Comparison of the yearly cycle cosine curve imitative parameters of the clinical features of liver cancer at different longitude and latitude].

OBJECTIVE: To explore whether there exists coincidence of the most appearing time of clinical features of liver cancer at different longitude and latitude, according to the law of field equation and the theory of warpage of space time by Einstein. METHODS: Three regions with different longitude and latitude were selected randomly and sampled. There were 36 items altogether, including 12 clinical items, which were used to imitate the yearly cycle cosine curve. The acorphases and the ratioes of amplitudes and means were compared to justifying whether they were in the same range. RESULTS: All the acorphases of 36 items appeared between -90.1degrees to -207.5 degrees (from april to july), existing in one third of the same range, in which 13 items occurred rhythmly (P<0.05). The image acorphases of liver cancer at the early and middle stage and gamma-glutamyl transpeptidase acorphase appeared between -98.5 degrees to -148.2 degrees (from april to may), in which 5 items occurred rhythmly (P<0.05). CONCLUSION: It is the same mode of the yearly biologcal cycle for liver cancer malignant growth within the most appearing time (from april to july). It will increase the detecting rate of liver cancer at the early and middle stage during this time (especially from april to may).